International Journal of Electrical and Computer Engineering (IJECE)
Vol. 8, No. 2, April 2018, pp. 979~988
ISSN: 2088-8708, DOI: 10.11591/ijece.v8i2.pp979-988
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Granular Mobility-Factor Analysis Framework for enriching
Occupancy Sensing with Doppler Radar
Preethi K. Mane, K. Narasimha Rao
Department of Electronics & Instrumentation Engineering, BMS College of Engineering, Bangalore, India
Article Info
ABSTRACT
Article history:
With the growing need for adoption of smarter resource control system in
existing infrastructure, the proliferation of occupancy sensing is slowly
increasing its pace. After reviewing an existing system, we find that
utilization of Doppler radar is less progressive in enhancing the accuracy of
occupancy sensing operation. Therefore, we introduce a novel analytical
model that is meant for incorporating granularity in tracing the psychological
periodic characteristic of an object by emphasizing on the mobility and
uncertainty movement of an object in the monitoring area. Hence, the model
is more emphasized on identifying the rate of change in any periodic
physiological characteristic of an object with the aid of mathematical
modelling. At the same time, the model extracts certain traits of frequency
shift and directionality for better tracking of the unidentified object behavior
where its applicabilibility can be generalized in majority of the fields related
to object detection.
Received Dec 26, 2017
Revised Mar 4, 2018
Accepted Mar 18, 2018
Keyword:
Accuracy
Doppler radar
Mobility
Occupation sensing
Random
Uncertainty
Copyright © 2018 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Preethi K. Mane,
Dept of Electronics & Instrumentation Engineering,
BMS College of Engineering,
Bangalore, India.
Email: pkmph2008@gmail.com
1.
INTRODUCTION
At present, in majority of the developing countries is known for its trendy, massive, and smart
commercial complex powered by large number of resources in order to make it ready for production floor.
One easiest means of automatically controlling, monitoring, and managing the existing resources in
commercial building is by deploying occupancy sensor [1]. At present, there are various forms of occupancy
sensor e.g. electromagnetic signal, image sensor, carbon-dioxide sensor, ultrasonic sensor, power meters, etc
[2], [3]. The methods employed by such sensors are both terminal and non-terminal wise [4]. The base
function of occupancy sensor is to involuntarily control the switching system of the resources in case of
unoccupied spaces. At present, there are two dominant techniques for sensing the level of occupancy i.e. by
using infrared sensor and ultrasonic sensor [5]. Basically, usage of infrared sensors resides in identifying any
form of change in temperature of the monitored spaces whereas the utilization of ultrasonic sensor is found
more on acoustics with high frequency in order to perform motion detection [6], [7]. There is also certain
hybrid level of sensors that uses both of these techniques for enhancing the detection rate with better
accuracy. However, such sensors are quite expensive in nature. There are certain set of research work which
has highlighted that positioning of occupancy sensor is extremely important for its successful operation. At
present, the classification of occupancy sensing process is essentially categorized in three forms (a) based on
function, (b) based on method, and (c) based on infrastructure. [8]. However, it is normally noted that
existing occupancy sensing mainly uses sensors where ultrasonic and microwave sensors are much preferred
owing to its equivalent operation in the form of radar with Doppler shift [9]. Basically, Doppler effect is
utilized in Doppler radar in order to generate the correct positioning of any object with its specific distance
Journal homepage: http://iaescore.com/journals/index.php/IJECE
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using microwave signal. Usage of Doppler radar is more in aviation industry, meteorology, healthcare,
ballistic, etc. Maximum usage of Doppler radar is less seen in occupancy sensing model owing to wavelength
range, longer distances of operation, lack of enhanced frame rate, etc. Even it offers significant amount of
problems in obtaining less than 1% of error in detection rate. Apart from this, performing system-on-chip
operation along with compression followed by analysis is still an open challenge with non-compatibility of
field upgradable. At the same time, there is also a contradiction of theory and practical usage. Occupancy
sensor was originally meant of resource (energy) consumption but in reality user doesn‟t prioritize much on
energy and emphasize more on their comfort. Majority of the upcoming application calls for instantaneous
detection of physiological factors of subject where demands of accuracy is more dominant over any other
issues e.g. resource consumption. We strongly believe that usage of Doppler radar can be truly used for
enhancing the performance of occupancy sensor. The research area is quite novel as there is no much
commercially success product on this idea of accuracy for occupancy sensing. Moreover, there is not much
work being carried out towards exploring the direction of mobility of an object in occupancy sensing using
Doppler radar. Therefore, we present an analytical model where Doppler radar as well as occupancy sensing
is integrated with an objective of accomplishing better accuracy. The significant contribution of the proposed
system is actually to incorporate more granularities in the mobility factor of the subject being traced by
occupancy sensor using Doppler radar. Section 1.1 discusses about the existing literatures where different
techniques of both occupancy sensing as well as utilization of Doppler radar are discussed for detection
schemes used followed by discussion of research problems in Section 1.2 and the proposed research
methodology as a significant solution in 1.3. Section 2 discusses about analytical model along with algorithm
description followed by discussion of result analysis in Section 3. Finally, the conclusive remarks are
provided in Section 4.
1.1. Background
This section discusses the existing research contribution towards enhancing the design of occupancy
sensing. Eedara et al. [10] have presented a technique of sensing occupancy using metadata of standard WiFi
interface and received signal strength. Study towards the dual-band based input-output based approach of
sensing subject is carried out by Iyer et al. [11]. Liu et al. [12] have used pyroelectric infrared sensors to
perform occupancy sensing using standard hidden Markov Model. The work carried out by Li et al. [13] has
introduced deep sensing technique using the recursive mechanism of a mathematical model. Cooley et al.
[15] have used a special form of capacitative sensor to perform detection irrespective of any illumination
condition. George et al. [16] have presented an integrated prototype framework using inductive as well as a
capacitative approach for designing occupancy sensor. Hossain and Champagne [17] have addressed the
similar sensing problem with the aid of wideband spectrum considering a case study of cognitive radio. It has
been seen that there are not much implementation using Doppler radar. On the other hand, the studies
towards Doppler radar have addressed a different set of problem. Most recently, identification of the target is
carried out by Cooper et al. [18] using an analytical model. Doppler Radar has also been reported to be used
in the identification of wall breathing as seen in the work of Aversano et al. [19]. Implementation of Doppler
radar towards phase-locked loop has been carried out by Mercuri et al. [20] to perform non-invasive
monitoring of vital signs. Saho et al. [21] have used Doppler Radar for classification of the gaits of a
different form of subjects in the form of simulation. Wang et al. [22] have presented a scheme for identifying
the wind component using Doppler radar. Zhao et al. [23] have carried out an implementation of Doppler
radar to perform detection of physiological characters mainly emphasizing on the low-power transceiver
design. The problems associated with the correction of the radius in Doppler Radius is addressed in the work
of Gao et al. [24] where radar of continuous wavelength of high precision is used. Gurbuz et al. [25] have
used a similar form of waves to perform detection of simple mobile objects using radar signatures. Ritchie
et al. [26] have performed feature extraction using multi-static waveforms of Doppler radar and was used for
classifying flying objects. Identification of respiratory patterns using Doppler radar was carried out by Lee
et al. [27]. The usage of Doppler radar of high frequency has also been reported in the work of Liao et al.
[28]. The identification of various significant information from the satellites has been carried out by Vega
et al. [29] where the Doppler radar is also equipped with double frequency as well as polarization. The work
carried out by Ren et al. [30] has performed identification of heartbeats using demodulation of the complex
signal for precise detection performance. Majority of the existing approaches using Doppler radar has been
tested in hardware-based approach with a lesser computational model while the majority of the studies
focuses on identification of the certain targets. The extracted research issues are briefly outlined in next
section.
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1.2. Research Problem
The significant research problems are as follows:
a. Existing techniques are much less on using Doppler radar for assisting in occupancy sensing
modeling and implementation.
b. Less computational approach and more hardware-based approach towards Doppler radar usage
render reduced scope in the presence of uncertainty and dynamic modeling.
c. Existing framework is capable of identifying the presence of certain objects; however, its
granularity towards investigating the physiological characteristics is completely missing.
d. A robust mathematical model for integrating occupancy sensing and usage of Doppler radar is
few to find in existing approaches.
Therefore, the problem statement of the proposed study can be stated as “Designing an approach for
offering better granularity in the mobility behavior of a moving object for enhanced occupancy sensing by
harnessing the potential of Doppler Radar."
Reading Input
Signal
Implicating Framing
And Windowing
Operation
Obtaining Local
Maxima:
Fast Fourier
Transform (FFT)
Operation
Identification of
biosignals
Generating
Input Signal
Performing
Filtering
Operation
Received
Signal
Spectrum
-ve Direction
Simulation of Doppler
Radars
+ve Direction
1.3. Proposed Solution
The prime purpose of the proposed system is to perform an effective modeling of applying Doppler
Radar for better occupational sensing. The contributions in the present work are a) inclusion of dynamic and
uncertain mobility of subject b) incorporation of granularity of mobility by incorporating directionality
factor. By the term granularity, it will mean that the proposed system not only perform detection of the
presence of any subject within the specified range of Doppler Radar but also computes its rate of specific
movement that is quite periodic. For simpler model construction, we consider to compute the rate of
respiration for the subject as well as its direction towards and away from the reference point (where the radar
resides). The flow of the proposed method is shown in Figure 1.
Directionality
Inclusion
Impact of Shifting
Operation
Figure 1. Proposed flow of Methodology
According to the proposed system (Figure 1), we first simulate the Doppler radar in order to perform
more granular investigation of the biological signal by reading its input signal followed by generating the
input signal performed by the filtering operation as well as implicating the framing & windowing operation
followed by the accomplishment of the local maxima and FFT operation. The prime logic of proposed study
is that there is a significant amount of shifting of frequency over the spectrum of input signal whenever a
subject performs any motion. Depending on the velocity of the mobility of the subject, the direction, as well
as score of frequency, can be computed. The proposed system, therefore, emphasize on the received signal
spectrum. We also apply a simple modulation-based framework that considers uncertain mobility issues to
trace the spectrum with shifted frequency. Hence, in a nutshell, the study will finally aim towards observing
the shifted frequency of the spectrum to compute the rate of any action related to the biological signal. An
analytical model is designed for this purpose where simpler mathematical modeling is applied for
constructing the identification of the subject as well as computation of the rate of respiration under the very
uncertain condition of mobility. The next section discusses the analytical model.
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2.
ANALYTICAL MODEL
This section discusses the analytical model being implemented towards designing a novel
occupation sensing system for uncertain and undynamic subject mobility using Doppler radar. The presented
analytical model makes use of the Doppler shift to track the presence of any forms of biological signals of
any object. The applicability of Doppler radar is quite high in this as according to theory, we can say that
there is a periodical mobility associated with an object with the same frequency. However, the object might
have a phase shift with time-varying nature, and therefore, it is much suitable for detection of any subject
with the biological signal in a non-invasive manner. A simple example to understand this analytical
implementation is the process of respiration that is characterized by periodic displacement of the chest.
Moreover, such mobility factors will introduce a highly varying phase shift better proportion to the chest
displacement considering signal wavelength. The proposed system applies modulation scheme of Binary
Phase Shift Keying (BPSK) although other forms of modulation could also experiment. The algorithm
develops a channel with MIMO form to construct channel object. The prime emphasize was offered in
developing a cost-effective implementation of identification process of biological signals of any inhabitants
of the specific area. Another uniqueness of proposed algorithm is that it doesn't offer any form of recursive
operation. Thus it leads to faster response time as well as lower computational complexity in the form of
storage complexity. All the computation is carried out at run-time, and there are no residual signals being left
behind in memory. The complete modeling is carried out using six discrete operations, i.e., performing a
simulation of Doppler radar, generating an input signal, performing filtering operation, implicating framing
and windowing operation, obtaining local maxima, and finally performing Fast Fourier Transform (FFT)
operation. Following are the brief outline of the proposed analytical model.
a. Performing Simulation of Doppler Radars
This algorithm is essentially meant for simulating the Doppler radar that is deployed for detecting
the presence of any new subject by their biological signal. The algorithm takes the input of S
(stream), M (order of modulation), Nt (transmitter antenna), Nr (receiver antenna), fd (maximum
Doppler shift for all path), ts (input sample period), τ (path delay), pbd (mean path gain), σ (antenna
coefficient), Nsam_f (samples per frame), and Nsamp (samples per frame) that after processing gives the
output of hs (Doppler spectrum). The steps included in the algorithm are:
Algorithm for Simulating Doppler Radars
Input: S, M, Nt, Nr, fd, ts, τ, pbd, σ, Nsam_f, Nsamp
Output: hs
Start
1. init S, M
2. h=mc(Nt, Nr, ts, fd, τ, pbd)
3. h.TxCMα(σ)
4. For iF=1:N frame
5. Sigin=mod(hmodem, arb(S, M), Nsam_f, Nt
6. idx=(1:Nsam_f)+(iF-1)*Nsamp_f;
7. filter(h, Sigin)
8. For i=1: Nt
9.
[y1, y2, y3, (idx, it)]=h.Pgain(it)
10. End
11. End
12. Show PSD(hs, „Nsamp/5‟)
End
The first step of the algorithm is to develop a set of local random number stream using Modified
subtract with borrow generator with a seed for generating the local stream S (Line-1). The algorithm
considers 2nd order of modulation M to create an object of BPSK modulation h modem(Line-5). The
input sample period ts is computed as follows:
 1

 R

sym*log2 ( M ) 
ts  
N os
(1)
In the above expression, the input bit rate is obtained by the product of input symbol rate R sym with
the logarithmic function of modulation order M. The variable Nos is a factor of oversampling. The
next part of the algorithm implementation is associated with the channel modeling by considering a
number of the factors as shown in Line-2. The essential factors are path delay τ, mean path gains (in
dB) pbd, maximum Doppler shift allocated for all paths fd, some transmitting / receiving antenna
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Nt/Nr, correlation coefficient that is equivalent to the antenna correlation σ. The channel object h is
created (Line-2), and we also derive a transmit correlation matrix shown in Line-3 using nonsymmetric Toeplitz matrix over the argument of antenna correlation. The study computes all the
number of the frames Nframe obtained by dividing a total number of channel samples N samp by some
samples per frame Nsamp_f. A loop is created for all the number of frames (Line-4), where the prime
task is to compute input signal Sigin, index idx, and output. The input signal is formed by applying
modulation function on modulation object hmodem, an arbitrary value of signal and modulation
order, sample frequency, and a number of the transmitter (Line-5). The index is formed by adding
all the individual sample frequency with the product of remaining frames with sample frequencies
(Line-6). The output is formed by applying filtering operation on channel object h and input signal
Sigin (Line-7). Another nested loop (Line-8) is created for all the number of transmitters where the
relative information of path gains Pgain concerning channel object h is computed respectively for y1,
y2, and y3 matrix (Line-9). Finally, a power spectral density psd is obtained (Line-12) to obtain the
simulation of Doppler radar.
b. Generating Input Signal
The input signal is formed by considering synthetic biological signal data and representing it in the
form of time (x-axis) and amplitude (y-axis).
c. Performing Filtering Operation
To perform filtering operation, the algorithm considers inclusion of a new variable δ that stands for
preemphasis coefficient. The filtered outcome F is obtained as follows:
F   ([1   ], xbio )
(2)
In the above equation, the function ϕ represents a filtering operation on xbio (vector derived from the
input signal) with the filter represented by [1- δ] and 1 in order to obtained filtered data F. The
preemphasis coefficient δ is used for maximizing the measure of certain frequency of higher order
for enhancing the cumulative signal quality. It is also responsible for controlling any form of
possible effects of signal saturation, distortion, or attenuation, etc.
d. Implicating Framing and Windowing Operation
As the proposed system considers biological signals to be non-stationary therefore for stabilized
computation, framing and windowing operation is highly essential. This algorithm takes the input of
δ (preemphasis coefficient), Tw (frame duration for analysis), Ts (frame shift for analysis), fs
(Sampling frequency) which after processing yields an output of E (envelop). The steps involved in
the algorithm are as follows:
Algorithm for Framing & Windowing
Input: δ, Tw, Ts, fs
Output: E
Start
1. init δ, Tw, Ts, fs
2. F   ([1   ], xbio )
3. [Nw Ns]1E-ψ, where ψ=3*Ts*fs
4. Frames  g v frame ( F , N w , N s )
5. E=[tmax tmin]β(F, c)
End
The initial steps of this algorithm are same as that of performing filter operation in the prior stage
(Line-1 to Line-2). The algorithm computes both durations as well as shifts of the given frame
(Line-3) and then applies a function gvframe to perform framing and windowing operation (Line-4).
This step is further followed by the computation of peak detection for facilitating windowing
operation concerning maximum tmax and minimum tmin value of the envelope E (Line-5) considering
the permissible envelop coefficient of c.
e. Obtaining Local Maxima
This process is responsible for computing the local maxima of the given biological signals of the
Dopler Radar. The primary intention of this step is to obtain the interpolated version of an envelope
obtained in the prior step. The complete operation of this implementation is similar to Line-1 to
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Line-4 of an algorithm for framing and windowing with an additional step of applying a function of
local maxima on obtained frames of Line-4 to correctly obtain upper and lower envelop.
f. Fast Fourier Transform (FFT) Operation
This is the last stage of the proposed implementation where FFT operation is carried out on frame F.
Accomplishment of this step confirmed the form of biological signals.
We use this model to generate the transmitted signal, that is normally unmodulated can be
represented empirically as:
Tsignal=cos(2πft+(π/180)*30))
(3)
The next phase of the implementation is to construct a received signal. We formulate an algorithm
that takes the input of c (velocity of light), f1 (frequency), a (heart mobility factor), b (respiratory
mobility factor), t1(time), v (velocity of object), dini (initial distance) in order to yield an output of im
(integrated mobility), R (received signal).
Algorithm for obtaining the received biosignal concerning Mobility
Input: c, f1, a, b, t1, v, dini
Output: im, R
Start
1. init dini, v, t1, a, b, f1, c
2. im = dini+ v.t1 + a + b
3. R=cos((2πft)-((4πd_t)/(c/f1)+ph)
End
The complete formulation of the biosignals is carried out by ignoring the variation in amplitude. At
the same time, we can signify the term vt1 as a positive or negative sign for showing the forward and
reverse the direction of mobility of the subject. The next part of the study is to formulate a spectrum
for this which is generated by considering a periodic mobility due to heart beat (a) as well as
respiration (b) in the form of [a b]=sin(2πfat). According to our formulation, the routine mobility of
the body is due to the heartbeat and respiration. This method is then continued by computing motion
modulation where we consider samples per second to compute the wave in the form of:
x=ω(fft(0.5.cos(2πFc.t)+cos(2πFc1.t)))
(4)
The function ω represent FFT shift operation that leads to the generation of the spectrum concerning
frequency and the absolute value of x/N, where N represents the number of the sample concerning
time. This process offers an empirical response to mobility in forwarding direction concerning
biological signal. The similar process is applied for computing mobility in a backward direction with
(t-t0) being replaced in the position of t in the above mentioned equation. We also compute the
actuator result for both before and after shift concerning DFT value and normalized frequency value.
According to our mobility model, the frequency spectrum will always be inclined towards +ve axis
if the object is found to be moving towards Doppler radar by 2v/wavelength, while it takes the
similar empirical value for reverse movement direction as it moves to the -ve axis.This formulation
of directionality is carried out to resist any forms of uncertainty in the mobility model. The
advantage of this model is that if the maximum peak is studied, it can give the true picture of its
shifting behavior towards positive/negative axis which is directly a representation of a minor
movement even like respiration that can be easily identified using the present model. At the same
time, the system minimizes the dependencies on multiple radar system as complete information of
mobility of an object can be tracked by the proposed system concerning its respective direction.
3.
RESULT ANALYSIS
The numerical values considered to carry out this analysis are: preemphasis coefficient δ of 0.01,
modulation order M=2, input of symbol rate 10000, oversampling factor N os=4, maximum Doppler shift
fd=0.5, 2 transmit antenna, and 1 receive antenna, average path gain of 0 dB, -15 dB, and -20 dB, correlation
coefficient σ=0.7, number of samples per frame Nsamp_f=1000, total channel samples=1.5x106. The
corresponding outcomes are shown in Figure 2.
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(a) Doppler Radar Simulation
(b) Input Signal
(c) Filtering Operation
(d) Framing & Windowing Operation
(e) Applying Local Maxima
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(f) FFT Operation
Figure 2. Graphical Outcome of Proposed System
The graphical outcome shown in Figure 2 shows an effective identification of the biological signal
that can effectively trace any form of dynamic or random mobility subjects. The complete computation time
is found to be 0.024487 seconds in core-i3 windows machine. The complete analysis is carried out using
synthetic respiratory data. The streamlined pattern of the curve in Figure 2(d) to Figure 2(f) shows that
proposed system can provide significant assists in extracting the rate of respiration with a higher probability
of lower resource dependencies. The next part of the experiment for mobility considers frequency as 10 6 Hz,
the speed of light as 3*108mps, samples per second as 100, Fc=5 Hz, and Fc1=0.1 Hz, time t0=1 sec. Figure 3
highlights that proposed system is completely capable of analyzing forward motion (Figure 3(a)) and
backward motion (Figure 3(b)). Similarly, a better visibility of the spectrum as an impact of shift operation
can be seen in Figure 3(c) and Figure 3(d). Owing to the non-recursive formulation of the complete technique
of occupancy sensing in the proposed system, there is a significant scope of better compatibility in lowGranular Mobility-Factor Analysis Framework for enriching Occupancy Sensing with …. (Preethi K. Mane)
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powered and low-cost hardware design in future. We have also performed computation concerning other
modulation scheme as well as by changing other simulation variables. However, in the majority of the cases
there is a uniformity in the detection performance, and thereby it exhibits the better reliability in its outcome
with an accuracy of 97.85%. Hence, the proposed system offers a cost-effective approach to model
occupation sensing concerning direction.
(a) Analysis of forwarding motion
(b) Analysis of backward motion
(c) Analysis Before shift
(d) Analysis After Shift
Figure 3 Analysis of Mobility Patterns
4.
CONCLUSION
Usage of occupancy sensing, as well as Doppler radar, is not novel from an individual viewpoint, but
the integrated usage of both is very less reported in existing literature. Although there is an existing system to
perform detection of a moving object, they miss granularity in modeling the mobility behavior. Therefore, the
proposed system introduces an analytical model that is used for computing the periodic physiological factor of
a mobile subject under the monitoring range of the radar. An explicit modulation technique is applied to study
directionality of object movement using the positive and negative axis of frequency. The study outcome shows
that proposed system offers better resolution of the waveforms for enriched investigation of the dynamic
mobility pattern of any object.
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ISSN: 2088-8708
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BIOGRAPHIES OF AUTHORS
Preethi K Mane was born in 1974 in Bangalore, India. She received her BE degree in
Instrumentation Technology, in the year 1996 from Bangalore University and ME in Electronics
from Bangalore University in the year 2000.She is pursuing Ph.D. in Electronics and
communication Engineering from Visvesvaraya Technological University. She has work experience
of 21years in teaching and is currently serving as Associate Professor in the Department of
Electronics and Instrumentation Technology.
Dr. K.Narasimha Rao has obtained his B.Tech, M.Tech and Ph.D. degrees in the year 1983, 1985
and 2011 respectively. He has 26 years of teaching experience. Presently, he is Professor
Department Of Instrumentation Technology, BMSCE, and Bangalore-19. His research interests are
in the areas of energy efficient DC Drives, Digital Signal Processing, Process Control and
Biomedical Signal Processing.
Int J Elec & Comp Eng, Vol. 8, No. 2, April 2018 : 979 – 988